_base_ = "../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py"
model = dict(
    pretrained="open-mmlab://msra/hrnetv2_w32",
    backbone=dict(
        _delete_=True,
        type="HRNet",
        extra=dict(
            stage1=dict(
                num_modules=1,
                num_branches=1,
                block="BOTTLENECK",
                num_blocks=(4,),
                num_channels=(64,),
            ),
            stage2=dict(
                num_modules=1,
                num_branches=2,
                block="BASIC",
                num_blocks=(4, 4),
                num_channels=(32, 64),
            ),
            stage3=dict(
                num_modules=4,
                num_branches=3,
                block="BASIC",
                num_blocks=(4, 4, 4),
                num_channels=(32, 64, 128),
            ),
            stage4=dict(
                num_modules=3,
                num_branches=4,
                block="BASIC",
                num_blocks=(4, 4, 4, 4),
                num_channels=(32, 64, 128, 256),
            ),
        ),
    ),
    neck=dict(
        _delete_=True,
        type="HRFPN",
        in_channels=[32, 64, 128, 256],
        out_channels=256,
        stride=2,
        num_outs=5,
    ),
)
img_norm_cfg = dict(
    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
